We develop a multivariate statistical arbitrage strategy based on vine copulas - a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and...

This study derives an optimal pairs trading strategy based on a Lévy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

Over the past 15 years,there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect...

Most statistical arbitrage strategies in the academic literature soley rely on price time series. By contrast, alternative data sources are of growing importance for professional investors. We contribute to bridging this gap by assessing the price-predictive value of more than nine million...

This paper develops the regime classification algorithm and applies it within a fully-edged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any...